Auditor Independence: A Nonparametric Test of Differences Across the Big-5 Public Accounting Firms
Bibliographic record
Abstract
This small sample study provides additional evidence on the unsettled question of auditor independence: Does the provision of non-audit services by an auditor compromise independence resulting in a poor quality audit? We also examine whether these findings vary across the “Big-5” public accounting firms. Most prior studies addressing this question, using parametric approaches and various measures of audit quality, have reported conflicting results. Contrary to these studies, we use a non-parametric approach and the probability of GAAP violation as a new measure of audit quality to address this question. Using data from a sample of Fortune 500 companies for the year 2000, we find that firms whose auditors provide substantial non-audit services tend to have a higher propensity to violate GAAP. At the firm-level analysis, we find that these results are more likely driven by few of the Big-5 public accounting firms. For the remaining firms, the association between non-audit services and quality of audit could not be established, primarily because of small sample size and lack of power in the test. Our main finding is consistent with other recent studies that provide evidence that the rendering of significant non-audit services by auditors creates conflict of interest resulting in poor quality audits. Furthermore, our result of differences in these levels of association among the Big-5 accounting firms represents a new finding, and suggests that there is a need for controlling them separately in research studies examining auditor independence.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".